Current Drug Discovery Technologies (v.10, #2)

Cancer is a deadly disease and a huge burden to the society. Although the last 60 years has seen improvements in cancer diagnostics, treatment strategies against most of the complex malignancies have not lived up to the mark. In the drug discovery area, the attrition rates have spiraled out of control, indicating that there is certainly something amiss employing the current research approaches against cancer. Advances in computational biology have revealed that cancer is a disease arising from aberrations in complex biological networks and its understanding requires more information than that obtained from the reductionist strategies. Similarly, magic bullet drugs that are designed against a single pathway may not impact these highly intertwined and robust cancer networks. In order to rein in cancer, one has to revamp the concepts in understanding the mechanism of cancer and drastically reform the present approaches to drug discovery. The idea behind this review is to enlighten the readers about the emerging concept of ‘Network Pharmacology’ in drug discovery. Network technologies have allowed not only in the rational targeting of aberrant signaling in cancer but also helped in understanding secondary drug effects. Concepts in network methods that are helping hit identification, lead selection, optimizing drug efficacy, as well as minimizing side-effects are discussed. Finally, some of the successful network-based drug development strategies are shown through the examples cancer.

Drugs and their Interactions by Murat Cokol (106-113).
Small molecules with selective efficacy can be used as drugs. Drugs typically have a therapeutic dose range where the benefit from intended effects outweighs the cost of unintended (side) effects. Herein, I use case scenarios to illustrate potential advantages and pitfalls of treatment with drugs alone or in combination. Combinations of drugs may show surprising effects given the effects of individual drugs, in phenomena known as drug interactions. Drug interactions can be classified as synergistic or antagonistic, if the effect of the combination is higher or lower than expected, respectively. A better understanding of drug interactions and their relationship to phenotypes offers hope in finding drug combinations that have high therapeutic values.

A Brief Survey on Computational Approaches to Reveal Drug and Disease Associations by Hsiang-Yuan Yeh, Wei-Chih Lin, Yu-Fen Huang, Von-Wun Soo (114-124).
People worldwide are still threatened by various complex disease phenotypes, especially cancer which is usually caused by the accumulation of multi-factor-driven alterations. Although drugs achieve the therapeutic functions by targeting particular molecular, the therapies used nowadays against diseases are not effective enough due to the limitation of the knowledge about the drug-disease associations. The rapid increasing of the available experimental data and knowledge enable scientists to reveal drug-disease associations by the systematic integration and analysis. In this review, we show that several computational methods can help us to explain the underlying relationships between pharmacology and pathology. It is expected that newer computational methods will take advantage of heterogeneous and multi-dimensional data and increase the efficacy and safety of existing drugs for disease treatment.

Network Pharmacology of Glioblastoma by Baltazar D. Aguda (125-138).
With increasing knowledge of cellular networks of gene and molecular interactions, and their alterations in GBM (glioblastoma multiforme), it is now possible to apply methods of Network Pharmacology (NP) to predict candidate drug targets for this malignant brain tumor. NP requires the development of mathematical methods for network stability and perturbation analysis to identify sensitive and druggable network components, as well as computational platforms to carry out in silico simulations of therapeutic interventions. This review focuses on the three most frequently deregulated GBM pathways involving membrane receptor tyrosine kinases, p53, and Rb. Structural features of these networks that may confound targeted therapies are discussed.

Systems Biology Approaches in Identifying the Targets of Natural Compounds for Cancer Therapy by Yi Tan, Qiong Wu, Jun Xia, Lucio Miele, Fazlul H Sarkar, Zhiwei Wang (139-146).
Natural compounds have been known to exert inhibitory effects on the development and progression of human cancers. However, the targets of these naturally occurring agents are largely elusive. Recently, systems biology approaches based on high-throughput technologies such as DNA microarrays have begun to be utilized for investigating the targets of drugs including natural compounds. Therefore, in this review article, we will briefly introduce the several systems biology approaches, and will discuss the application of these new technologies for identifying the therapeutic targets of natural compounds for supporting their roles in the prevention and/or treatment of human cancers. Furthermore, identification of the novel targets will be useful for designing more effective and targeted therapeutic strategies for achieving better treatment outcome in patients diagnosed with cancers.

Network Insights into the Genes Regulated by Hepatocyte Nuclear Factor 4 in Response to Drug Induced Perturbations: A Review by Asfar S. Azmi, Ginny W. Bao, Jiankun Gao, Ramzi M. Mohammad, Fazlul H. Sarkar (147-154).
Transcription factors (TFs) play central role in normal cellular physiology and their aberrant expression is linked to different diseases. Hepatocyte Nuclear Factors (HNFs) are TFs that have been recognized to play multiple roles in liver physiology. Emerging research has highlighted their function in the sustenance of solid tumors, indicating that HNFs could serve as possible therapeutic targets in cancer. Although, there have been many attempts to develop HNF targeted drugs, the myriad downstream targets associated with these transcription factors, some of which are critical for normal cell homeostasis, led to the realization that HNFs are not easily druggable. Therefore, identifying and optimizing drugs that can selectively inactivate HNFs is a challenge to the pharmaceutical industry. To achieve this, a more in-depth understanding is required of the HNFs binding partners, the protein interaction networks it regulates and the resulting phenotype. This calls for network analysis of the pathways regulated by HNFs and how chemical perturbations can selectively activate or suppress their functions. Network biology is an emerging field of research that is finding applications in cancer drug discovery. Specifically, network pharmacology is cementing its position in cancer research and has various applications such as biomarker identification, in determining synergistic drug pairs and in drug repurposing. Developing a network understanding of HNFs, the target it hits and responses thereof can enhance our ability to design drugs against these TFs. This article reviews how network pharmacology can help in the identification of druggable avenues in TFs and also allow the selection of drugs and their synergistic pairs against HNFs for cancer therapy.

Network Pharmacology: Reigning in Drug Attrition? by Osama M. Alian, Minjel Shah, Momin Mohammad, Ramzi M. Mohammad (155-159).
In the process of drug development, there has been an exceptionally high attrition rate in oncological compounds entering late phases of testing. This has seen a concurrent reduction in approved NCEs (new chemical entities) reaching patients. Network pharmacology has become a valuable tool in understanding the fine details of drug-target interactions as well as painting a more practical picture of phenotype relationships to patients and drugs. By utilizing all the tools achieved through molecular medicine and combining it with high throughput data analysis, interactions and mechanisms can be elucidated and treatments reasonably tailored to patients expressing specific phenotypes (or genotypes) of disease, essentially reigning in the phenomenon of drug attrition.

The use of fluorescent technologies in neurosurgery has a substantial history with applications to vascular and tumor surgery dating back to the 1940s. This review focuses on the applications of fluorescence imaging to intracranial vascular and neoplastic lesions using sodium fluorescein. The authors performed a literature search for articles about the use of sodium fluorescein in neurosurgery. Fifty-five articles were initially retrieved, and 37 of these were appropriate for this review. The subcategorization of these articles revealed 2 describing the properties of fluorescein, 19 articles relating to applications of fluorescein to tumor, 11 relating to vascular applications, and 5 reporting side effects associated with fluorescein use. Articles related to use of this agent in evaluation of CSF leak were excluded. Sodium fluorescein has been reported to be a useful surgical adjunct in resection of neoplastic lesions based on differential fluorescence between normal and neoplastic tissue. There are many reports on the utility of fluorescein in vascular imaging relating to arteriovenous malformations, aneurysms, and vessel anastomosis; however, these reports do not examine primary outcomes. Sodium fluorescein has been judged as generally safe with few reports of severe complications. Sodium fluorescein has demonstrated promise as a useful surgical adjunct in neurosurgery for vascular and neoplastic lesions. It is well tolerated, but further study is required to determine its full utility. Finally, we will introduce a new practical technology that could potentially improve intraoperative application of sodium fluorescein by improving its fluorescence visualization while using substantially lower doses of this dye.

Treatment of Lung Cancer Using Nanoparticle Drug Delivery Systems by Vijay Chandolu, Crispin R. Dass (170-176).
Context: One of the leading causes of cancer-associated deaths in most men and women in the Western world is lung cancer. There are various types of treatments depending on the type and the stage of the cancer. A recent type of therapy is targeted gene therapy which aims to target genes that cause lung cancer. However, this therapy has some drawbacks including lack of proper vectors for delivery. These drawbacks can potentially be overcome by using various types of nanoparticles. Objective: To review current literature on the treatment of lung cancer with nanoparticles. Methods: Researchers have attempted to treat lung cancer with a variety of types of nanoparticle matrices including lipid, polylactide-co-glycolide, albumin, poly (ω-pentadecalactone-co-butylene-co-succinate), cerium oxide, gold, ultra-small superparamagnetic iron oxide nanoparticles, super paramagnetic iron oxide, lipid–polycation–DNA, N-[1-(2,3- dioleoyloxyl)propyl]-NNN-trimethylammoniummethylsulfate, silica-overcoated magnetic cores, and polyethyleneglycol phosphatidylethanolamine. There are various ways in which nanoparticles enhance drug delivery, and these include encapsulation against immune response, tissue penetration, target selectivity and specificity, delivery monitoring, promoting apoptosis, and blocking pathways for cancer initiation and progression. Conclusion: In the past decade, a lot has been said about targeting of NPs for lung and other cancers, but little has been actually successfully delivered to date. Nevertheless, nanoparticles can act as good vectors for delivering drug to the target neoplastic lesions within the lung, increase cellular uptake, increase tissue penetration and help in tracking the drug. In the future, combination therapies may play a key role in the treatment of lung cancer using the existing therapies.